Collaborative electronic nose management in personal devices

ABSTRACT

A system and diagnosis server are provided for collaborating with electronic noses, as well as a related mobile diagnosis unit and related method. The diagnosis server includes a receiver unit for receiving a set of data from one e-nose of a plurality of e-noses. The set of data may include a sensor identifier, a sensor output value, and a relevance flag for a predefined diagnosis. In addition, a determination unit determines a probability factor for the predefined diagnosis based on the set of data, a relevance function and a distribution function.

BACKGROUND

The present invention relates generally to a system including adiagnosis server for collaboration with electronic noses, and to amobile diagnosis unit for collaboration with the diagnosis server. Theinvention relates further to a diagnosis method for collaborating usingelectronic noses.

The field of electronic noses is becoming one of the more interestingtechnologies from a research and investment point of view. Electronicnoses (in short: e-nose) keep airports and, for instance, the spacestation, safe by noticing the tiniest amounts of dangerous chemicals.They can tell the difference between things like different beveragesthat taste the same, bad apples and good ones, cancer cells and normalcells, even different qualities of wine may be detectable by electronicnoses. All of them use a number of sensors, but even the moresophisticated ones have no more than eight sensors simultaneouslyactive. Besides, due to higher volume and reduced prices, more unitswill be sold and used.

Price and physical volume are key issues when e-noses are used in smartdevices, like smart mobile phones, and the like.

In most cases, software analyzes the patterns of each sensor todetermine what the smell is, but the software is only able to elaboratea static set of input values, the one provided by the sensors, and in apre-defined range. Every time a sensor is not able to report a value, orthe combination of sensors values is out of this pre-defined range, thee-nose is not able to provide an answer. For these reasons, e-noses aresmall in number and mainly used in big organizations like hospitals oruniversities. Earlier prototypes of low price e-noses to be embedded inpersonal devices are showing less effectiveness in the sense that theyare equipped with a lower number of sensors and each sensor has anarrower range of appliance.

There is a growing need for more advanced e-nose based diagnosis deviceswith more sophisticated methods, also for lower cost e-noses, which mayhave a limited measurement range.

SUMMARY

Provided herein, in one or more aspects, is a system for collaboratingwith electronic noses. The system includes a memory, and a processorcommunicatively coupled to the memory, wherein the system performs amethod comprising: receiving a set of data from one e-nose of aplurality of e-noses, the set of data including a sensor identifier, asensor output value, and a relevance flag for a predetermined diagnosis,wherein the relevance flag is indicative of a usefulness of the sensoroutput value for the predefined diagnosis; and determining a probabilityfactor for the predetermined diagnosis based on the set of data, arelevance function, and a distribution function, wherein the relevancefunction is indicative of the relevance of the sensor output value forthe predefined diagnosis, and the distribution function is indicative ofa distribution of the sensor output value for the predefined diagnosis.

In a further aspect, a mobile diagnosis unit is provided which includes:a user interface to receive a predefined diagnosis; an e-nose sensor; atransmission unit to transmit a set of data, the set of data comprisinga sensor identifier, a sensor output value, and a relevance flag for apredefined diagnosis, wherein the relevance flag is indicative of ausefulness of the sensor output value for the predefined diagnosis; areceiving unit to receive a total probability P(d) for the predefineddiagnosis; and wherein the user interface provides notice of the totalprobability P(d) for the predefined diagnosis.

In a further aspect, a diagnosis method for collaborating usingelectronic noses is provided. The diagnosis method includes: receiving aset of data from one e-nose of a plurality of e-noses, the set of datacomprising a sensor identifier, a sensor output value, and a relevanceflag for a predefined diagnosis, wherein the relevance flag isindicative of a usefulness of the sensor output value for the predefineddiagnosis; and determining a probability factor for the predefineddiagnosis based on the set of data, a relevance function and adistribution function, wherein the relevance function is indicative ofthe relevance of the sensor output value for the predefined diagnosis,and the distribution function is indicative of a distribution of thesensor output value for the predefined diagnosis.

BRIEF DESCRIPTION OF THE DRAWINGS

Several embodiments of the invention are described below, by way ofexample only, with reference to the drawings, in which:

FIG. 1 shows an exemplary block diagram of a diagnosis server, inaccordance with one or more aspects of the present invention;

FIG. 2 shows an exemplary block diagram a process flow on a mobilediagnosis unit, in accordance with one or more aspects of the presentinvention;

FIG. 3 shows an exemplary block diagram and process flow on a diagnosisserver, in accordance with one or more aspects of the present invention;

FIG. 4 shows an exemplary block diagram of an embodiment of a mobilediagnosis unit, in accordance with one or more aspects of the presentinvention;

FIG. 5 shows an exemplary block diagram of an embodiment of a mobilephone comprising a mobile diagnosis unit, in accordance with one or moreaspects of the present invention;

FIG. 6 shows a block diagram of an embodiment of a computer comprising adiagnosis server, in accordance with one or more aspects of the presentinvention; and

FIG. 7 shows a block diagram of a method for collaborating withelectronic noses, in accordance with one or more aspects of the presentinvention.

DETAILED DESCRIPTION

In the context of this description, the following conventions, termsand/or expressions may be used:

The term “diagnosis server” may denote a component of a computer adaptedto perform an analysis based on input from one or more sensor data. Thesensor data may actually be measured sensor data. Additionally, earliermeasured sensor data may have been stored in a database. The diagnosisserver may comprise a number of sophisticated analysis algorithmsfunctions. It may also incorporate none-sensor-data into the analysisdetermination.

The term “electronic nose” or, in short “e-nose” may denote a sensor,generating output signals, based on different chemical componentspresent for the sensor. One e-nose may comprise several sensors.Different e-noses or different sensors in one e-nose may be required fordifferent chemicals on different ranges of chemical concentrations.Another name for an e-nose may be “smell sensor”. Different e-noses maybe adapted to detect different chemical components in a gas mixture oran aerosol. An aerosol may be a suspension of fine solid particles orliquid droplets in a gas.

The term “diagnosis” may denote a certain or predefined state of healthor illness of a person or an animal. A diagnosis may represent adetermination regarding a certain health status.

The term “predefined diagnosis” may denote a guess for a diagnosis by auser of a mobile diagnosis unit. The user may input his guess for hisactual diagnosis into the mobile diagnosis unit, whereby it becomes apredefined diagnosis. E.g., a predefined diagnosis may be “Cold?”,meaning “do I have a cold”, or does a person have a cold.

The term “relevance flag” may denote a flag being set to “0” or “1” bythe mobile diagnosis unit. During a baseline measurement for thesensor(s), the usefulness of certain e-nose measurements including theirrespective output values may be set in relation to a certain diagnosisfor a person. The mobile diagnosis unit may, e.g., comprise threesensors. It may turn out that the output values of the sensor 3 may notbe useful for a diagnosis at all. However, sensor 2 of the three sensorsmay measure a chemical component that may be relevant and useful for thediagnosis, but the concentration of the chemical component to bemeasured may cause an out of range situation for the sensor. However,the usefulness of the sensor value may be set to “1”, meaning “true”.Indicators about the usefulness regarding a specific diagnosis may behandled within the mobile diagnosis unit. They may have been determinedusing server side computing of a diagnosis server. However, typicallythey are static in contrast to a dynamic probability determination basedon a diagnosis server side diagnosis determination.

The proposed diagnosis server for collaborating with electronic noses,the mobile diagnosis unit for collaboration with a diagnosis server, therelated devices in the related method may offer a couple of advantages:

Traditional mobile diagnosis units may rely on logic, incorporated intothe mobile diagnosis unit. One or more e-noses may be used for aconfirmation of a predefined diagnosis. However, such a method may notmake effective usage of other diagnoses from other mobile diagnosisunits and its respective users. The here disclosed devices and methodsare based on a collaboration between a diagnosis server and a mobilediagnosis unit. The mobile diagnosis unit may have a plurality ofe-noses and/or sensors, or there may be more than one mobile diagnosisunits contributing to a final diagnosis. In addition, historic diagnosesmay be reflected using sensor output values and respective diagnosisusing a database as a basis for a relevance function and a distributionfunction.

A diagnosis being based on these two functions may be more precise thana single unit diagnosis system with fixed, static diagnosis patterns.

The interaction between a mobile diagnosis unit and a diagnosis server,in particular using cloud technologies, allows for an overallprobability expression for a diagnosis for a person (or animal).

Such a technique may allow a community of people to build a relevanceand distribution function for each disease, and associate the troublethe user may be experiencing, the time frame in which it happens, thegeographical location, and other environmental parameters.

When a user may request an evaluation or diagnosis for a givenmeasurement of a sensor of the e-nose, a diagnosis server sideapplication may interpolate the provided measured data with ann-dimensional function of the disease in the community, to build aprobabilistic report and correlate which diseases the values may berelated to. The “experience of the cloud” may be used, so to speak.

If not enough information is available for a server side diagnosis, theuser of the mobile diagnosis unit may be presented a list of questionsfor more information. Thus, the user and the system get in a dialoguehelping to close information gaps for a most probable diagnosis. In caseit is needed, the diagnosis server may inform the user about the mostcommon troubles associated with the most probable disease in form of achecklist that the user may give a feedback on and further filter theresults. For this purpose, the diagnosis system may transmit the relatedinformation from the diagnosis server to the mobile diagnosis unit.

The more users with mobile diagnosis units may be in collaboration withthe diagnosis server, in particular wirelessly, and the more diagnosesmay be performed, the more precise a probability for a predefineddiagnosis may be performed. In this sense, a system comprising thediagnosis server and a plurality of mobile diagnosis units may be seenas a self-learning and optimizing system.

According to one embodiment of the diagnosis server, the set or sets ofdata may comprise in addition environmental data which comprise at leastone out of the group consisting of a photo, a time stamp, geographicalcoordinates, temperature, humidity, altitude. This may add more decisioncriteria to algorithms available in the diagnosis server. Symptoms for aspecific disease may vary according to temperature and/or high humidityand/or attitude. There may also be a concentration of specific diseaseswithin a geographical region, so that a probability for a specificdiagnosis for a person located in an environment of that geographicalregion may be higher. Using picture recognition and analysis, adiagnosis may be made more specific, e.g., for skin diseases.

According to a further embodiment of the diagnosis server each set ofdata receivable from the plurality of e-noses or sensors may be storedin a database for further reference, i.e., for future diagnoses. Thesefuture diagnoses may relate to predetermined diagnoses of the sameperson or other persons of the community. This way, the gainedexperience in the diagnosis server may be available for each communitymember. Additionally, a distribution function for all measured sensoroutput values in relationship to a specific diagnosis may be built. Thesame applies to a relevance function. A resulting probability factor fora diagnosis may be much more precise (see below).

According to an additional embodiment of the diagnosis server, thedetermination unit may be adapted to determine a value for thedistribution function based on one or more of the sensor output valuesin comparison to all stored sensor output values in the database for thepredefined diagnosis. The distribution function may basically representa percentage of sensor values in the database having the same sensoroutput value for the same predefined diagnosis. This way, historicallymeasured sensor output values in correlation with the predefineddiagnosis may be correlated with an actual sensor output value of agiven set of data. Again, the experience of the whole community may beused for a diagnosis determination.

According to a further enhanced embodiment of the diagnosis server, thedetermination unit may be adapted to determine the probability factorP(s) for a diagnosis based on

P(s)=RF(v(s))*DF(v(s)),

wherein

s=sensor identifier,

v(s)=sensor output value,

RF is the relevance function,

DF is the distribution function.

The relevance function and the distribution function, each beingindependent on the sensor output value of a specific sensor “s”, mayhave values between “0” and “1”. Thus, a resulting probability may alsohave a value between “0” and “1”, expressing a correlation between aspecific predetermined diagnosis and a specific sensor output value oftheir related sensor.

According to an even more advanced embodiment of the diagnosis server,the determination unit may be also adapted to determine the totalprobability P(d) for the predetermined diagnosis based on

P(d)=P(s1)*P(s2)* . . . *P(sm),

wherein

d=predetermined diagnosis,

s1=sensor 1, s2=sensor 2, sm=sensor m.

In this case, a plurality of sensor output values from a plurality ofsensors may be involved. The source of the values may originate fromdifferent e-noses from one mobile diagnosis unit all from severaldifferent mobile diagnosis units. This may deliver a great flexibilityin used e-noses and sensors. If, e.g., a user may have only a mobilediagnosis unit in, e.g., his mobile phone, and a user may also use amore advanced mobile diagnosis unit in a medical centre or a communitycentre, the additionally delivered sets of data to the diagnosis serverfor a predefined diagnosis may enhance the precision of the diagnosis.

The data for the different sensors within a given timeframe may be usedfor the relevance function, and thus, for the determination of the totalprobability P(d). It may also be noted that the relevance flag (from themobile diagnosis unit) and the relevance function (from the diagnosisserver) may be different entities with different functions.

According to one more embodiments of the diagnosis server, there may bea transmission unit available. It may be attached to the diagnosisserver. The transmission unit may be adapted to transmit the totalprobability for a predetermined diagnosis, in particular, to the mobileunit in a wireless way. Moreover, the complete communication between aplurality of mobile diagnosis units and the diagnosis server may bebased on any wireless or wire-based, or a mixture of both,communication.

According to one advantageous embodiment of the mobile diagnosis unit,it may comprise a plurality of e-nose sensors. Actually, each mobilediagnosis unit may have a different number of sensors. They may beoptimized for detecting different chemical compounds.

According to one further embodiment of the mobile diagnosis unit, theset of data generated by the mobile diagnosis unit may compriseenvironmental data generated by at least one out of the group consistingof a camera, a clock, geographical coordinate sensor, temperaturesensor, humidity sensor, altitude sensor. The effects have beendiscussed above already. In addition, it may be mentioned that thecorrelation between specific sensor output values and a predefineddiagnosis may vary by the time of the day.

It should also be noted that embodiments of the invention have beendescribed with reference to different subject-matters. In particular,some embodiments have been described with reference to method typeclaims whereas other embodiments have been described with reference toapparatus type claims. However, a person skilled in the art will gatherfrom the above and the following description that, unless otherwisenotified, in addition to any combination of features belonging to onetype of subject-matter, also any combination between features relatingto different subject-matters, in particular, between features of themethod type claims, and features of the apparatus type claims, isconsidered as to be disclosed within this document.

The aspects defined above and further aspects of the present inventionare apparent from the examples of embodiments to be describedhereinafter and are explained with reference to the examples ofembodiments, but to which the invention is not limited.

In the following, a detailed description of the figures will be given.All instructions in the figures are schematic. Firstly, a block diagramof an embodiment of the inventive diagnosis server for collaboratingwith electronic noses, and mobile diagnosis units for collaboration witha diagnosis server is given. Afterwards, further embodiments and adiagnosis method for collaborating with electronic noses are described.

FIG. 1 shows an exemplary block diagram of a diagnosis server 100. Thediagnosis server 100 may comprise a receiver unit 102 adapted to receivea set of data receivable from one out of a plurality of e-noses. Some ofthe e-nose measurement values may originate from one mobile diagnosisunit, and others may originate from several mobile diagnosis units.

The received set of data may comprise a sensor identifier, in particulara unique identifier of a specific sensor. All sensors may have theirunique identifier. The set of data may also comprise a sensor outputvalue, in particular a measured value indicative of a measuredconcentration of a chemical compound. Furthermore, the set of data mayalso comprise a relevance flag for a predefined diagnosis. A user mayhave to put in the predetermined diagnosis as a question. The input mayhave been done in several ways: by typing on a keyboard, by typing on atouch-sensitive user interface, by voice recognition, by gesturerecognition, by selecting from a menu within a user interface, and thelike.

The relevance flag may be a general indication for the usefulness of aspecific sensor output for a predefined diagnosis. Such a relevanceindication may be stored in the mobile diagnosis unit. It may be sensorand/or diagnosis dependent. Values of the relevance flag may be“0”—meaning not relevant for a predefined diagnosis—or “1” meaningrelevant for a predefined diagnosis.

Thus, the relevance flag may be indicative of a usefulness of the sensoroutput value for the predefined diagnosis. However, the sensor may beout of range for a useful measurement meaning that the sensor outputvalue may be useless in such a case, but the relevance flag may still be“true”.

Moreover, the diagnosis server may comprise a (diagnosis) determinationunit 104 adapted for determining a probability factor for the predefineddiagnosis based on the set of data, a relevance function and adistribution function, as explained above. The relevance function may beindicative of the relevance of the sensor output value for thepredefined diagnosis. The distribution function may be indicative of adistribution of the sensor output value for the predefined diagnosis.

Furthermore, a transmission unit may be available as discussed below incontext of FIG. 3.

FIG. 2 shows an exemplary block diagram and process flow 200 on themobile diagnosis unit side. A related software program may start at 202.Firstly, the unit may have to obtain data, 204: a) sensor data and b) adiagnosis question. A user may select a diagnosis question relating to aspecific diagnosis. This may be performed by free text input or byselecting from a menu in the user interface. Moreover, voice input maybe possible. The received spoken word for the diagnosis question may beanalyzed and recognized either on the mobile unit or on a related serversystem. Next, at 206, it may be decided whether a specific diagnosis isrequired. If not, the received values from the e-nose sensor may besent, 224, or transmitted to a related diagnosis server 100 (FIG. 1).Here, the data may be stored for further reference and/or for baselinemeasurement. The program may end at 216.

In case a user requests a specific diagnosis, the sensor data may besent, 208, to the diagnosis server 100 (FIG. 1). Together with a sensordata, sensor identifier and a relevance flag for the specified diagnosismay also be sent to the diagnosis server.

At the diagnosis server 100 (FIG. 1) side, an answer may be determinedand may be received by the mobile diagnosis unit, 210. If a diagnosishas been received because the server has determined that the diagnosisis unique, then the result of the diagnosis may be displayed, 214, atthe mobile diagnosis unit. The program may end at 216.

In case the received answer from the diagnosis server 100 (FIG. 1) maynot be a unique diagnosis, the diagnosis server 100 may send a series ofquestions such that a questionnaire may be displayed at 218 to the user.

A couple more steps (not shown in the flowchart) may be required: theuser may answer the questions, the questions may be transmitted to thediagnosis server 100 (FIG. 1), the diagnosis server 100 may bere-determining its diagnosis and may send it back to the mobilediagnosis unit. In case the re-determined diagnosis is received from theserver, (same as 212), the diagnosis may be displayed, 214.

Alternatively, the mobile diagnosis unit may compute the diagnosisitself, 220, and send information back, 222, to the diagnosis server 100(FIG. 1). It may also be possible that the mobile diagnosis unit mayreceive instructions, e.g., in form of a markup language, how tocompute, 220, the diagnosis.

FIG. 3 shows an exemplary flowchart 300 executed by a control system aspart of the diagnosis server 100 (FIG. 1). The program starts at 302.Firstly, the system may receive data, 304, from the mobile diagnosisunit. The data may comprise data only or, there may also be a diagnosisrequest included. As a minimum, the server may receive a sensor outputvalue from sensor m, together with an identifier of sensor m, as well asoptionally a related relevance flag for a predefined diagnostic. Thismay be received by the diagnosis server 100 (FIG. 1) in a data set. Incase no diagnosis may be requested, the data may be stored for laterreference, 320, in a database of the diagnosis server. This may comprisenot only the sensor value, but also other received information like asensor identifier, and other environmental data like geographicalposition (GPS coordinates), geographical altitude, humidity,temperature, a timestamp and/or a photo. After that, the program may endat 330.

In case a diagnosis may be requested, the next steps may be performed:the relevance function, 306, and the distribution function, 308, may becalculated. Additionally, in step 310, the probability P(d) for apredetermined diagnosis may be calculated, 312. For this, the values ofadditional sensors may be used, as explained above. All results may bebuilt into a probability report for the diagnosis based on n sensorvalues and also values from the database of earlier sensor values andrelated diagnoses.

If a diagnosis has been requested, 314, by the mobile diagnosis unit andthe computed diagnosis of the diagnosis server 100 (FIG. 1) may beunique, 316, then the diagnosis server may send the results of thediagnosis computation to the mobile diagnosis unit, 318. This may bedone using the transmission unit 106 (see FIG. 1). The program may endat 330.

In case the diagnosis may not be unique, 316, the diagnosis server maygenerate, 322, a questionnaire to be sent, 324, to the mobile diagnosisunit via the transmission unit 106 of the diagnosis server 100 (see FIG.1). All transmissions from the mobile diagnosis unit to the diagnosisserver and back may be performed wirelessly using a public or privatewireless network. At the mobile diagnosis unit, the questions of thequestionnaire may be answered and sent back to the diagnosis server,were they may be received, 326.

A couple more steps (not shown in the flowchart) may be required: thediagnosis server 100 may be re-determining its diagnosis and may send itback to the mobile diagnosis unit. The re-determined diagnosis may bethen received (212) and displayed on the mobile display unit (214).

Also this information may be stored, 328, in the database of thediagnosis server 100 (FIG. 1). It may be used to further reference foranother diagnosis. The program may end at 330.

FIG. 1 shows an exemplary block diagram of an embodiment of mobilediagnosis unit 400. The mobile diagnosis unit 400 for collaboration witha diagnosis server 100 (FIG. 1) may comprise a user interface 402adapted for receiving a predetermined diagnosis and other input. A usermay choose a variety of ways to input the predefined diagnosis. Anon-complete list of examples may comprise: using a keyboard or atouch-sensitive screen, using voice input or selecting from a menu.Also, more sophisticated gesture input and/or gesture recognition ispossible.

Moreover, the mobile diagnosis unit 400 may also comprise at leaste-nose sensor 404 and a transmission unit 406 adapted to transmit a setof data and related data. The set of data may comprise a sensor 404identifier, a sensor 404 output value, and a relevance flag for apredefined diagnosis, as defined above.

A receiving unit 408 may be adapted to receive a total probability P(d)for the predetermined diagnosis, in particular from the diagnosis server100 (FIG. 1). The communication between the unit and the server may beperformed wirelessly or, wire based.

The user interface 402 may also be adapted to notify about the totalprobability P(d) for the predefined diagnosis. In particular, the“notify about” may be made to a user. A display may be instrumental forsuch a purpose. However, also voice output or, tactile output usingspecial devices for disabled people may be possible, just to name a few.

It may be noted here, that the relevance flag is something notstatically derived by the sensor 404 output value. It's like a secondlevel analysis about how much that sensor 404 is relevant for the givendiagnosis. It may also happen, for example, that the sensor 404 outputvalue is not in the range for a diagnosis, but its relevance flag may betrue.

The following scenario may be helpful in this context: Every time ameasurement may show a value for a sensor 404 that may be out of rangefor a given diagnosis, the diagnosis itself may not be evaluatedpositively (for example, if the e-nose is queried for a certain diseaseand s2 may show a value that may not be in the range for that disease,the e-nose may answer that the user may not be affected by it). However,among all the measurements leading to negative diagnosis, there may becases in which some of the sensors 404 of an e-nose show values in arange for a given disease. For example, if someone wants to check hisbreath for a disease D1 and he uses a traditional e-nose, or if he usesa personal device and he knows he is not affected, he may have s2 and s4in range for D1, and all other sensors 404 out of range. So, the answerwill be negative (he is not affected by D1) but the system may computes2 and s4 as “not relevant” for D1, while the other sensors 404 may berelevant. The relevance of a sensor 404 may be computed also in anothercase. During the personal device life, a series of measurements may beperformed by a user in a good health state, and a range of “normal”values for each sensor 404 is built for him and stored in a database. Assoon as an “out of range” value is detected, a warning is issued and(likely) the user will perform some exams or will directly enter thedisease he's suffering, together with the troubles he's experiencing. Inthis case, the system will consider the out of range sensor(s) 404 asrelevant for that disease, even for diseases for which the e-nose is notmeant to provide any answer.

FIG. 5 shows a mobile device 500, like a smart phone, equipped with amobile diagnosis unit 400 (FIG. 4). The mobile device 500 may comprise atouch sensitive display unit 502. In this case, the screen of thedisplay unit 502 may also be used for user input. Additionally, themobile device 500 may comprise keys or other switches 504 for inputpurposes. Furthermore, the mobile device 500 may be equipped with acentral possessing unit, memory, a battery, and other elements typicallyare for today's mobile devices, like smart phones. All of theseadditional elements may be referred to by reference number 506.Additionally, the mobile device 500 may comprise the mobile diagnosisunit 400 (FIG. 4). The mobile device 500 and the mobile diagnosis unit400 (FIG. 4) may use common element. For example, the receiving unit 408(FIG. 4) of the mobile diagnosis unit 400 and/or the transmission unit406 may be equivalent to those elements of the mobile device 500 (FIG.5). However, the transmission unit 406 and a receiving unit 408 may alsobe seen as an integral part of the mobile diagnosis unit. In this case,there may be an interface between the mobile device 500 and the mobilediagnosis unit being adapted for handling the transmission andreceiving.

FIG. 6 shows a block diagram of a computing system 600 with typicalelements also comprising the diagnosis server 100 (FIG. 1).

Embodiments of the invention may be implemented together with virtuallyany type of computer, regardless of the platform being suitable forstoring and/or executing program code. For example, as shown in FIG. 6,a computing system 600 may include one or more processor(s) 602 with oneor more cores per processor, associated memory elements 604, an internalstorage device 606 (e.g., a hard disk, an optical drive such as acompact disk drive or digital video disk (DVD) drive, a flash memorystick, a solid-state disk, etc.), and numerous other elements andfunctionalities, typical of today's computers (not shown). The memoryelements 604 may include a main memory, e.g., a random access memory(RAM), employed during actual execution of the program code, and a cachememory, which may provide temporary storage of at least some programcode and/or data in order to reduce the number of times, code and/ordata must be retrieved from a long-term storage medium or external bulkstorage (archive 616) for an execution. Elements inside the computer 600may be linked together by means of a bus system 618 with correspondingadapters. As shown, the diagnosis server may also be attached to the bussystem 618. However, it may also be integrated into the computer 600 ina different, e.g., distributed form.

The computing system 600 may also include input means, such as akeyboard 608, a pointing device such as a mouse 610, or a microphone(not shown). Alternatively, the computing system may be equipped with atouch sensitive screen as main input device. Furthermore, the computer600, may include output means, such as a monitor or screen 612, (forinstance, a liquid crystal display (LCD), a plasma display, a lightemitting diode display (LED), or cathode ray tube (CRT) monitor). Thecomputer system 600 may be connected to a network (e.g., a local areanetwork (LAN), a wide area network (WAN), such as the Internet or anyother similar type of network, including wireless networks via a networkinterface connection 614. This may allow a coupling to other computersystems or a storage network or a tape drive. Those, skilled in the artwill appreciate that many different types of computer systems exist, andthe aforementioned input and output means may take other forms. Ingeneral, the computer system 600 may include at least the minimalprocessing, input and/or output means, necessary to practice one or moreembodiments of the present invention.

Actually, the mobile phone 500 (FIG. 5) may be equipped with basicallythe same active elements as the diagnosis server 100 (FIG. 1). Only thesize of the active computing elements may be adapted to the size of themobile phone. Instead of the diagnosis server 100 (FIG. 1), the mobilephone may be equipped with the mobile diagnosis unit 400 (FIG. 4). Andinstead of a pointing device 610, a touch sensitive screen may take overan equivalent function. The keyboard 608 may also be replaced by a touchsensitive screen as a personal with ordinary skills may know.

FIG. 7 shows a block diagram of a diagnosis method 700 for collaboratingusing electronic noses. The diagnosis method 700 may comprise receiving,702—e.g., at a diagnosis server 100—a set of data receivable from oneout of a plurality of e-noses 404 (FIG. 4). The set of data may comprisea sensor 404 identifier, a sensor 404 output value, and a relevance flagfor a predefined diagnosis. Also here, the relevance flag may indicativeof a usefulness of the sensor 404 output value for the predefineddiagnosis.

Moreover, the method may comprise determining, 704, a probability factorfor the predefined diagnosis based on the set of data, a relevancefunction and a distribution function. The relevance function may beindicative of the relevance of the sensor 404 output value for thepredefined diagnosis, and the distribution function may be indicative ofa distribution of the sensor 404 output value for the predefineddiagnosis.

After the determination 704, the probability factor for a predetermineddiagnosis may be transmitted, 706, in particular to a mobile unit and/ora diagnosis unit.

As described above, a diagnosis server is provided herein forcollaboration with electronic noses, a mobile diagnosis unit, and adiagnosis method for collaborating with electronic noses according tothe claims. Parts of the solution may also be embedded into a computer,a mobile phone or a system.

According to one embodiment, a diagnosis server for collaboration withelectronic noses may be provided. The diagnosis server may comprise areceiver unit adapted to receive a set of data receivable from one outof a plurality of e-noses. Each set of data may comprise a sensoridentifier, a sensor output value, and a relevance flag for a predefineddiagnosis. The relevance flag may be indicative of a usefulness of thesensor output value for the predefined diagnosis.

The diagnosis server may also comprise a determination unit adapted fordetermining a probability factor for the predefined diagnosis based onthe set of data, a relevance function and a distribution function. Therelevance function may be indicative of the relevance of the sensoroutput value for the predefined diagnosis, and the distribution functionmay be indicative of a distribution of the sensor output values for thepredefined diagnosis.

According to another embodiment, a mobile diagnosis unit forcollaboration may be provided. The mobile diagnosis unit may comprise auser interface adapted to receive a predetermined diagnosis, an e-nosesensor, and a transmission unit adapted to transmit sets of data. Eachset of data may comprise a sensor identifier, a sensor output value, anda relevance flag for a predefined diagnosis. The relevance flag may beindicative of a usefulness of the sensor output value for the predefineddiagnosis.

The mobile diagnosis unit may also comprise a receiving unit adapted toreceive a total probability P(d) for the predetermined diagnosis, inparticular from a diagnosis server. The user interface may also beadapted to notify about the total probability P(d) for the predefineddiagnosis, in particular to a user of the mobile diagnosis unit.

It may be noted that the mobile diagnosis unit may comprise more thanone sensor and from each sensor a set of data may be provided.

According to yet another embodiment, a diagnosis method forcollaborating with electronic noses may be provided. The diagnosismethod may comprise receiving a set of data receivable from one out of aplurality of e-noses. The sets of data may each comprise a sensoridentifier, a sensor output value, and a relevance flag for a predefineddiagnosis. The relevance flag may be indicative of a usefulness of thesensor output value for the predefined diagnosis.

The method may additionally comprise determining a probability factorfor the predefined diagnosis based on the set, or in particular sets ofdata, a relevance function and a distribution function. The relevancefunction may be indicative of the relevance of the sensor output valuefor the predefined diagnosis. The distribution function is indicative ofa distribution of the sensor output value for the predefined diagnosis.

Furthermore, a diagnosis system, comprising the diagnosis server and themobile diagnosis unit, as well as a mobile phone comprising the mobilediagnosis unit, and a computer comprising the diagnosis server, may beprovided.

It may be noted that the function of the diagnosis server may bedelivered as a cloud computing service, wherein the diagnosis server mayserve a plurality of mobile diagnosis units using standardized Webprotocols. The capability provided to a consumer, e.g., a user of amobile diagnosis unit may be to use the provider's applications runningon a cloud infrastructure. The applications may be accessible fromvarious client devices, in particular, mobile diagnosis units, through athin client interface such as a Web browser. The consumer may not manageor control the underlying cloud infrastructure including network,servers, operating systems, storage, or even individual applicationcapabilities, with the possible exception of limited user-specificapplication configuration settings. The provider's service may be basedon the disclosed diagnosis server.

While the invention has been described with respect to a limited numberof embodiments, those skilled in the art, having benefit of thisdisclosure, will appreciate that other embodiments may be devised, whichdo not depart from the scope of the invention, as disclosed herein.Accordingly, the scope of the invention should be limited only by theattached claims. Also, elements described in association with differentembodiments may be combined. It should also be noted that referencesigns in the claims should not be construed as limiting elements.

As will be appreciated by one skilled in the art, aspects of the presentdisclosure may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present disclosure may take theform of an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present disclosure may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that may contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that may communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present disclosure are described with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of thepresent disclosure. It will be understood that each block of theflowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, may beimplemented by computer program instructions. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

These computer program instructions may also be stored in a computerreadable medium that may direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions, whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions, which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The block diagrams in the Figures illustrate the architecture,functionality, and operation of possible implementations of systems,methods and computer program products according to various embodimentsof the present disclosure. In this regard, each block in the blockdiagrams may represent a module, segment, or portion of code, whichcomprises one or more executable instructions for implementing thespecified logical function(s). It should also be noted that, in somealternative implementations, the functions discussed hereinabove mayoccur out of the disclosed order. For example, two functions taught insuccession may, in fact, be executed substantially concurrently, or thefunctions may sometimes be executed in the reverse order, depending uponthe functionality involved. It will also be noted that each block of theblock diagrams, and combinations of blocks in the block diagrams, may beimplemented by special purpose hardware-based systems that perform thespecified functions or acts, or combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to limit of the invention. As usedherein, the singular forms “a”, “an” and “the” are intended to includethe plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or steps plus function elements in the claims below are intendedto include any structure, material, or act for performing the functionin combination with other claimed elements, as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skills in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skills in the art to understand the invention forvarious embodiments with various modifications, as are suited to theparticular use contemplated.

1. A system for collaborating with electronic noses, the systemcomprising: a memory, and a processor communicatively coupled to thememory, wherein the system performs a method comprising: receiving a setof data from one e-nose of a plurality of e-noses, the set of dataincluding: a sensor identifier; a sensor output value; a relevance flagfor a predefined diagnosis, wherein the relevance flag is indicative ofa usefulness of the sensor output value for the predefined diagnosis;and determining a probability factor for the predefined diagnosis basedon the set of data, a relevance function and a distribution function,wherein the relevance function is indicative of the relevance of thesensor output value for the predefined diagnosis, and the distributionfunction is indicative of a distribution of the sensor output value forthe predefined diagnosis.
 2. The system of claim 1, wherein the set ofdata comprises environmental data which includes at least one of aphoto, a time stamp, geographical coordinates, temperature, humidity, oraltitude.
 3. The system of claim 1, wherein each set of data receivedfrom the plurality of e-noses is stored in a database.
 4. The system ofclaim 3, wherein the determining ascertains a value for the distributionfunction based on the sensor output value in comparison to stored sensoroutput values in the database for the predefined diagnosis.
 5. Thesystem of claim 1, wherein the determining ascertains the probabilityfactor P(s) based on:P(s)=RF(v(s))*DF(v(s)), wherein s=sensor identifier; v(s)=sensor outputvalue; RF is the relevance function; and DF is the distributionfunction.
 6. The system of claim 5, wherein the determining ascertains atotal probability P(d) for a predetermined diagnosis based on:P(d)=P(s1)*P(s2)* . . . *P(sm), wherein d=predetermined diagnosis; ands1=sensor 1, S2=sensor 2, sm=sensor m.
 7. The system of claim 6, furthercomprising transmitting the total probability for the predetermineddiagnosis.
 8. The method of claim 7, wherein at least one of thereceiving or the transmitting comprises wirelessly receiving orwirelessly transmitting, respectively.
 9. A mobile diagnosis unitcomprising: a user interface to receive a predetermined diagnosis; ane-nose sensor; a transmission unit to transmit a set of data, the set ofdata comprising: a sensor identifier; a sensor output value; and arelevance flag for a predefined diagnosis, wherein the relevance flag isindicative of a usefulness of the sensor output value for the predefineddiagnosis; a receiving unit to receive a total probability P(d) for thepredetermined diagnosis; and wherein the user interface provides noticeof the total probability P(d) for the predefined diagnosis.
 10. Themobile diagnosis unit of claim 9, further comprising a plurality ofe-nose sensors.
 11. The mobile diagnosis unit of claim 9, wherein theset of data comprises environmental data generated by at least one of acamera, a clock, geographical coordinate sensor, temperature sensor,humidity sensor, or altitude sensor. 12-14. (canceled)
 15. A diagnosismethod for collaborating using electronic noses, the diagnosis methodcomprising: receiving a set of data from one e-nose of a plurality ofe-noses, the set of data comprising: a sensor identifier; a sensoroutput value; and a relevance flag for a predefined diagnosis, whereinthe relevance flag is indicative of a usefulness of the sensor outputvalue for the predefined diagnosis; and determining a probability factorfor the predefined diagnosis based on the set of data, a relevancefunction and a distribution function, wherein the relevance function isindicative of the relevance of the sensor output value for thepredefined diagnosis, and the distribution function is indicative of adistribution of the sensor output value for the predefined diagnosis.16-17. (canceled)
 18. The mobile diagnosis unit of claim 9, wherein themobile diagnosis unit is implemented within a mobile phone.
 19. Themethod of claim 15, wherein the set of data comprises environmental datawhich includes at least one of a photo, a time stamp, geographicalcoordinates, temperature, humidity, or altitude.
 20. The method of claim15, wherein each set of data from the plurality of e-noses is stored ina database.
 21. The method of claim 20, wherein the determiningascertains a value for the distribution function based on the sensoroutput value in comparison to stored sensor output values in thedatabase for the predefined diagnosis.
 22. The method of claim 15,wherein the determining ascertains the probability factor P(s) based on:P(s)=RF(v(s))*DF(v(s)), wherein s=sensor identifier; v(s)=sensor outputvalue; RF is the relevance function.
 23. The method of claim 22, whereinthe determining ascertains a total probability P(d) for a predetermineddiagnosis based on:P(d)=P(s1)*P(s2)* . . . *P(sm), wherein d=predetermined diagnosis; ands1=sensor 1, S2=sensor 2, sm=sensor m.
 24. The method of claim 23,further comprising transmitting the total probability for thepredetermined diagnosis.
 25. The method of claim 24, wherein at leastone of the receiving or the transmitting comprises wirelessly receivingor wirelessly transmitting, respectively.